{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Using Gaussian processes for regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "import numpy as np\n",
    "boston = load_boston()\n",
    "boston_X = boston.data\n",
    "boston_y = boston.target\n",
    "train_set = np.random.choice([True, False], len(boston_y),p=[.75, .25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianProcessRegressor(alpha=1e-10, copy_X_train=True, kernel=None,\n",
       "             n_restarts_optimizer=0, normalize_y=False,\n",
       "             optimizer='fmin_l_bfgs_b', random_state=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "gpr = GaussianProcessRegressor()\n",
    "gpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process.kernels import RBF, ConstantKernel as CK\n",
    " \n",
    "mixed_kernel = kernel = CK(1.0, (1e-4, 1e4)) * RBF(10, (1e-4, 1e4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpr = GaussianProcessRegressor(alpha=5,\n",
    "                                n_restarts_optimizer=20,\n",
    "                                kernel = mixed_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpr.fit(boston_X[train_set],boston_y[train_set])\n",
    "\n",
    "test_preds = gpr.predict(boston_X[~train_set])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xacea6d8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p5VYp5Xz3fZOU8iIp5Vgp5cVSykH9bVReDgEBWuR5OGGymYgOjUYfoh+2FTe0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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa6c1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    " \n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    " \n",
    "f, ax = plt.subplots(figsize=(10, 7), nrows=3)\n",
    "f.tight_layout()\n",
    " \n",
    "ax[0].plot(range(len(test_preds)), test_preds,label='Predicted Values');\n",
    "ax[0].plot(range(len(test_preds)), boston_y[~train_set],label='Actual Values');\n",
    "ax[0].set_title(\"Predicted vs Actuals\")\n",
    "ax[0].legend(loc='best')\n",
    " \n",
    "ax[1].plot(range(len(test_preds)),test_preds - boston_y[~train_set]);\n",
    "ax[1].set_title(\"Plotted Residuals\")\n",
    "ax[2].hist(test_preds - boston_y[~train_set]);\n",
    "ax[2].set_title(\"Histogram of Residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jdavila\\Anaconda27\\lib\\site-packages\\sklearn\\gaussian_process\\gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'warnflag': 2, 'task': 'ABNORMAL_TERMINATION_IN_LNSRCH', 'grad': array([ -1.55105021e+01,  -1.97123069e-04]), 'nit': 7, 'funcalls': 51}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    " \n",
    "gpr5 = GaussianProcessRegressor(alpha=5,\n",
    "                                n_restarts_optimizer=20,\n",
    "                                kernel = mixed_kernel)\n",
    " \n",
    "scores_5 = (cross_val_score(gpr5,\n",
    "                             boston_X[train_set],\n",
    "                             boston_y[train_set],\n",
    "                             cv = 4,\n",
    "                             scoring = 'neg_mean_absolute_error'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of scores:  [ -4.00502184  -5.54413744  -3.9479669  -19.31000505]\n",
      "Mean of scores:  -8.20178280773\n",
      "Std of scores:  6.44521978982\n"
     ]
    }
   ],
   "source": [
    "def score_mini_report(scores_list):\n",
    "     print \"List of scores: \", scores_list\n",
    "     print \"Mean of scores: \", scores_list.mean()\n",
    "     print \"Std of scores: \", scores_list.std()\n",
    "     \n",
    "score_mini_report(scores_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of scores:  [ -3.64217     -5.12206477  -3.97728175 -17.87460448]\n",
      "Mean of scores:  -7.65403024969\n",
      "Std of scores:  5.92630763848\n"
     ]
    }
   ],
   "source": [
    "gpr7 = GaussianProcessRegressor(alpha=7,\n",
    "                                n_restarts_optimizer=20,\n",
    "                                kernel = mixed_kernel)\n",
    " \n",
    "scores_7 = (cross_val_score(gpr7,\n",
    "                             boston_X[train_set],\n",
    "                             boston_y[train_set],\n",
    "                             cv = 4,\n",
    "                             scoring = 'neg_mean_absolute_error'))\n",
    "score_mini_report(scores_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of scores:  [ -4.44760613  -7.8272561   -3.96961908 -19.79938367]\n",
      "Mean of scores:  -9.0109662447\n",
      "Std of scores:  6.40372073366\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    " \n",
    "gpr7n = GaussianProcessRegressor(alpha=7,\n",
    "                                n_restarts_optimizer=20,\n",
    "                                kernel = mixed_kernel,\n",
    "                                normalize_y=True)\n",
    " \n",
    "scores_7n = (cross_val_score(gpr7n,\n",
    "                             boston_X[train_set],\n",
    "                             boston_y[train_set],\n",
    "                             cv = 4,\n",
    "                             scoring = 'neg_mean_absolute_error'))\n",
    "score_mini_report(scores_7n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jdavila\\Anaconda27\\lib\\site-packages\\sklearn\\gaussian_process\\gpr.py:457: UserWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'warnflag': 2, 'task': 'ABNORMAL_TERMINATION_IN_LNSRCH', 'grad': array([ -1.75411925e+01,   1.06146453e-05]), 'nit': 7, 'funcalls': 52}\n",
      "  \" state: %s\" % convergence_dict)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GaussianProcessRegressor(alpha=7, copy_X_train=True,\n",
       "             kernel=1**2 * RBF(length_scale=10), n_restarts_optimizer=20,\n",
       "             normalize_y=True, optimizer='fmin_l_bfgs_b',\n",
       "             random_state=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpr7n.fit(boston_X[train_set],boston_y[train_set])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_preds = gpr7n.predict(boston_X[~train_set])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpr_new = GaussianProcessRegressor(alpha=boston_y[train_set]/4,\n",
    "                                  n_restarts_optimizer=20,\n",
    "                                  kernel = mixed_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.68823681,   2.70357163,  13.59415512,   2.45728218,   0.87065486])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds, MSE = gpr7n.predict(boston_X[~train_set], return_std=True)\n",
    "MSE[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DXVehez5jnTXGUlAwvH3dZHFzI+52lmxg165fANljpVosmYQVLkvSuBXyJmfJh1kVMr5z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\nZzFypO6HGDZMR/Ol2v8Zvs2zz+ohE2b5qlX6nb3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1oMz2b7zRncceG0Fx8ZiQ80mGWBYX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb585f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7, 5))\n",
    "n = MSE.shape[0]\n",
    "rng = range(n)\n",
    "ax.scatter(rng, test_preds[:n])\n",
    "ax.errorbar(rng, test_preds[:n], yerr=1.96*MSE[:n])\n",
    "ax.set_title(\"Predictions with Error Bars\")\n",
    "ax.set_xlim((-1, n));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
